where \(\mathbf {M}\)  indicates 0-1 mask that 1 means masked regions; \(\odot \)  means the element-wise multiplication; \(\mathbf {\hat{I}},\mathbf {\tilde{I}}\)  indicate the ground truth and predicted images respectively. The adversarial loss is consisted of the discriminator loss \(\mathcal {L}_D\)  and the generator loss \(\mathcal {L}_G\) . Moreover, we only regard features from masked regions as fake samples in \(\mathcal {L}_D\) . The PatchGAN [1]} based discriminator is written as \(D\)  and the combination of FTR and SFE can be seen as the generator \(G\) , Then the adversarial loss can be indicated as
\(\begin{split}\hspace{0.0pt}\hspace{0.0pt}\mathcal {L}_{D}=&-\mathbb {E}_{\mathbf {\hat{I}}}\left[\mathrm {log}D(\mathbf {\hat{I}})\right]-\mathbb {E}_{\mathbf {\tilde{I}},\mathbf {M}}\left[\mathrm {log}D(\mathbf {\tilde{I}})\odot (1-\mathbf {M})\right]\\&-\mathbb {E}_{\mathbf {\tilde{I}},\mathbf {M}}\left[\mathrm {log}(1-D(\mathbf {\tilde{I}}))\odot \mathbf {M}\right],\\&\mathcal {L}_{G}=-\mathbb {E}_{\mathbf {\tilde{I}}}\left[\mathrm {log}D(\mathbf {\tilde{I}})\right],\\&\mathcal {L}_{adv}=\mathcal {L}_D+\mathcal {L}_G+\lambda _{GP}\mathcal {L}_{GP},\end{split}\) 
